How to Avoid Data Leakage in Machine Learning: Target Encoding
Target encoding looks like a simple way to handle categorical features, but a naive implementation quietly leaks the target into the training set. Validation metrics look great, yet the model fails in production. This article explains where the leakage comes from and shows correct implementations using Leave-One-Out and K-Fold, so you get an honest result instead of pretty numbers on a dashboard.
AI-processed from Habr AI; edited by Hamidun News
Target encoding is a machine learning method for encoding categorical features, but a naive implementation systematically causes data leakage and inflates a model's test metrics, and then the model falls apart in production.
What is target encoding in machine learning?
Target encoding replaces each unique value of a categorical feature with the mean of the target variable for that category. For example, each city is assigned the average sales figure across customers from that city.
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